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Chord and Sankey diagrams are two common techniques for visualizing flows. Chord diagrams use a radial layout with a single circular axis, and Sankey diagrams use a left-to-right layout with two vertical axes. Previous work suggests both strengths and weaknesses of the radial approach, but little is known about the usability and interpretability of these two layout styles for showing flow. We carried out a study where participants answered questions using equivalent Chord and Sankey diagrams. We measured completion time, errors, perceived effort, and preference. Our results show that participants took substantially longer to answer questions with Chord diagrams and made more errors; participants also rated Chord as requiring more effort, and strongly preferred Sankey diagrams. Our study identifies and explains limitations of the popular Chord layout, provides new understanding about radial vs. linear layouts that can help guide visualization designers, and identifies possible design improvements for both visualization types.
Color is one of the main visual channels used for highlighting elements of interest in visualization. However, in multi-class scatterplots, color highlighting often comes at the expense of degraded color discriminability. In this paper, we argue for context-preserving highlighting during the interactive exploration of multi-class scatterplots to achieve desired pop-out effects, while maintaining good perceptual separability among all classes and consistent color mapping schemes under varying points of interest. We do this by first generating two contrastive color mapping schemes with large and small contrasts to the background. Both schemes maintain good perceptual separability among all classes and ensure that when colors from the two palettes are assigned to the same class, they have a high color consistency in color names. We then interactively combine these two schemes to create a dynamic color mapping for highlighting different points of interest. We demonstrate the effectiveness through crowd-sourced experiments and case studies.
How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation—via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, finding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies potential features that could be investigated in human correlation perception studies. Together, this paper suggests that deep neural networks can serve as a tool for visualization perception researchers in devising potential empirical study designs and hypothesizing about perpetual judgments. The preprint, data, code, and training logs are available at https://doi.org/10.17605/osf.io/exa8m.
Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers’ abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people's perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data.
In recent years, considerable work has been devoted to explaining predictive, deep learning-based models, and in turn how to evaluate explanations.
An important class of evaluation methods are ones that are human-centered, which typically require the communication of explanations through visualizations.
And while visualization plays a critical role in perceiving and understanding model explanations, how visualization design impacts human perception of explanations remains poorly understood.
In this work, we study the graphical perception of model explanations, specifically, saliency-based explanations for visual recognition models.
We propose an experimental design to investigate how human perception is influenced by visualization design, wherein we study the task of alignment assessment, or whether a saliency map aligns with an object in an image.
Our findings show that factors related to visualization design decisions, the type of alignment, and qualities of the saliency map all play important roles in how humans perceive saliency-based visual explanations.
Selecting appropriate visual encodings is critical to designing effective visualization recommendation systems, yet few findings from graphical perception are typically applied within these systems. We observe two significant limitations in translating graphical perception knowledge into actionable visualization recommendation rules/constraints: inconsistent reporting of findings and a lack of shared data across studies. How can we translate the graphical perception literature into a knowledge base for visualization recommendation? We present a review of 59 papers that study user perception and performance across ten visual analysis tasks. Through this study, we contribute a JSON dataset that collates existing theoretical and experimental knowledge and summarizes key study outcomes in graphical perception. We illustrate how this dataset can inform automated encoding decisions with three representative visualization recommendation systems. Based on our findings, we highlight open challenges and opportunities for the community in collating graphical perception knowledge for a range of visualization recommendation scenarios.